E-commerce
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Kroger and Nuro Announce Expanded Collaboration, Showcasing New Autonomous Vehicles Set to Power Grocery Delivery Service
In 2018, Kroger, America’s largest grocery retailer, and Nuro, leading autonomous vehicle company, announced a partnership to deliver fresh groceries with all-electric, autonomous vehicles. “Our expanded collaboration with Nuro supports Kroger’s commitment to provide fresh food, at a great value – all without asking our customers to compromise,” said Yael Cosset, Kroger’s Senior Vice President and Chief Information Officer. “The role of autonomous vehicles in our seamless ecosystem continues to increase, contributing to meeting our customers in the context of their day without compromising on the quality or value, while contributing to our long-term growth and sustainability goals.”
Leveraging Nuro’s new third-generation vehicles, Kroger will continue to grow its digital offerings in Houston, one of the largest cities in the U.S., building on its commitment to anything, anytime, anywhere. Grocery delivery through autonomous vehicles is a leading-edge e-commerce solution that offers customer-focused convenience – regardless of basket size.
How pioneering deep learning is reducing Amazon’s packaging waste
Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. These tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.
“When the model is certain of the best package type for a given product, we allow it to auto-certify it for that pack type,” says Bales. “When the model is less certain, it flags a product and its packaging for testing by a human.” The technology is currently being applied to product lines across North America and Europe, automatically reducing waste at a growing scale.
Predicting Defrost in Refrigeration Cases at Walmart using Fourier Transform
As the largest grocer in the United States, Walmart has a massive assembly of supermarket refrigeration systems in its stores across the country. Food quality is an essential part of our customer experience and Walmart spends a considerable amount annually on maintenance of its vast portfolio of refrigeration systems. In an effort to improve the overall maintenance practices, we use preventative and proactive maintenance strategies. We at Walmart Global Tech use IoT data and build algorithms to study and proactively detect anomalous events in refrigeration systems at Walmart.
Overcoming barriers to B2B eCommerce
Manufacturers now need to focus on evolving their strategies and leveraging digital solutions to meet B2B buyer needs. This will require specialist expertise to overcome complex challenges, from legacy technology constraints, entering new markets and the need to replicate offline experiences online.
Watco identified the need to migrate its traditional buyers online to gain a competitive advantage and cater for its growing digitally savvy audience. A new eCommerce website was launched with country-specific variants for the UK, France, Ireland and Germany. The flexible and scalable platform provided a configurable framework, free from constraints allowing Watco to continue to evolve and innovate whilst meeting the changing needs of their business and customers.
The evolution of Amazon’s inventory planning system
Forecasting models developed by Amazon’s Supply Chain Optimization Technologies organization predict the demand for every product. Buying systems determine the right level of product to purchase from different suppliers, while large-scale placement systems determine the optimal location for products across the hundreds of facilities belonging to Amazon’s global fulfillment network.
“In 2016, Amazon’s supply chain network was designed for scenarios where inventory from any fulfillment center could be shipped to any customer to meet a two-day promise,” said Salal Humair, senior principal research scientist at Amazon who has been with the company for seven years. This design was inadequate for the new world in which Amazon was operating; one shaped by what Humair calls the “globalization-localization imperative.”
A new multi-echelon inventory system developed by SCOT (a project whose roots stretch back to 2016) is a significant break from the past. The heart of the model is a multi-product, multi-fulfillment center, capacity-constrained model for optimizing inventory levels for multiple delivery speeds, under a dynamic fulfillment policy. The framework then uses a Lagrangian-type decomposition framework to control and optimize inventory levels across Amazon’s network in near real-time.
Broadly speaking, decomposition is a mathematical technique that breaks a large, complex problem up into smaller and simpler ones. Each of these problems is then solved in parallel or sequentially. The Lagrangian method of decomposition factors complicated constraints into the solution, while providing a ‘cost’ for violating these constraints. This cost makes the problem easier to solve by providing an upper bound to the maximization problem, which is critical when planning for inventory levels at Amazon’s scale.
In Amazon’s Flagship Fulfillment Center, the Machines Run the Show
More than the physical robots, the stars of Amazon’s facilities are the algorithms—sets of computer instructions designed to solve specific problems. Software determines how many items a facility can handle, where each product is supposed to go, how many people are required for the night shift during the holiday rush, and which truck is best positioned to get a stick of deodorant to a customer on time. “We rely on the software to help us make the right decisions,” says Shobe, BFI4’s general manager.
When managers wanted to figure out how many people they needed at each station to keep up with customer orders, they once used Excel and their gut. Then, starting in about 2014, the company flew spreadsheet jockeys from warehouses around the country to Seattle and put them in a conference room with software engineers, who distilled their work and automated it. The resulting AutoFlow program was clunky at first, spitting out recommendations to put half an employee at one station and half an employee at another, recalls David Glick, a former Amazon logistics executive who supervised initial development of the software. Eventually the system learned that humans can’t be split in half.
How Amazon's Middle Mile team helps packages make the journey to your doorstep
“To give you an idea of the scale and complexity we’re managing, our trucking network alone presents us with over 1088 — or ten octovigintillion — possible routing solutions,” says Tim Jacobs, director of Middle Mile Research Science and Optimization. “This is an especially large number, when you consider that there are 1082 atoms in the visible universe.”
And that’s just for the trucking network.
When a product is ordered on the Amazon Store, there are several ways it can make its way from a fulfillment center to the customer’s residence.
How Robotic Automation Impacts E-Commerce
AI and machine learning technologies are enabling new applications. In fact, most of the applications in ecommerce/fulfillment require some type of machine vision. However, with the huge proliferation of SKUs, the old way of programming for a particular part or object discretely is much more difficult to figure out what item to pick next. AI and machine learning will provide more opportunities for companies to expand their capabilities and help ease the burden of dealing with high levels of product variability.